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Intermediate → Advanced 5–7 months 5 Stages

The Computer Vision Engineer Roadmap

Computer Vision is one of AI's most impactful fields — powering everything from autonomous vehicles and medical imaging to augmented reality and manufacturing quality control. This roadmap takes you from CNN fundamentals through modern architectures (Vision Transformers, Diffusion Models) to real-time deployment and specialization.

Who This Is For
Developers and ML practitioners specializing in image and video understanding
Time Commitment
5–7 months
Difficulty
Intermediate → Advanced
Stages
5 stages, 15 resources

Prerequisites

Python proficiency
Basic ML and deep learning understanding
Linear algebra (matrix operations, transformations)
Experience with PyTorch or TensorFlow

The Roadmap

1

Image Processing & CNN Fundamentals

3–4 weeks

Build a strong foundation in digital image processing and convolutional neural networks. Understand how computers represent and manipulate images, master the convolution operation, and learn the classic CNN architectures (LeNet, AlexNet, VGG, ResNet) that established the field.

Digital image fundamentals — pixels, channels, color spaces, resolution
Image processing with OpenCV — filtering, edge detection, transformations
Convolutional Neural Networks — convolution, pooling, stride, padding
Classic architectures — LeNet, AlexNet, VGG, ResNet, and their innovations
Transfer learning — using pre-trained ImageNet models for custom tasks
Image classification project — build and train from scratch then with transfer learning
Free Course Stanford CS231N: CNNs for Visual Recognition Free Tutorial PyTorch Vision Tutorials Reference OpenCV Documentation
Computer Vision Convolutional Neural Network (CNN) Deep Learning (DL) Transfer Learning Activation Function Backpropagation Batch Normalization Dropout
2

Object Detection & Segmentation

4–5 weeks

Go beyond classification to localization, detection, and segmentation. Learn the evolution from R-CNN to YOLO and understand anchor-based vs anchor-free approaches. Master both instance segmentation (Mask R-CNN) and semantic segmentation (U-Net, DeepLab). Build projects that detect and segment objects in images and video.

Object detection fundamentals — bounding boxes, IoU, NMS
Two-stage detectors — R-CNN, Fast R-CNN, Faster R-CNN
One-stage detectors — YOLO (v5-v8), SSD, RetinaNet
Instance segmentation — Mask R-CNN and its variants
Semantic segmentation — U-Net, DeepLab, FCN
Panoptic segmentation — unifying instance and semantic approaches
Evaluation metrics — mAP, IoU thresholds, COCO metrics
Free Reference Ultralytics YOLOv8 Documentation Free Framework Detectron2 (Meta) Dataset COCO Dataset
Object Detection Computer Vision Convolutional Neural Network (CNN) Classification Precision & Recall Data Augmentation
3

Vision Transformers & Modern Architectures

3–4 weeks

The Transformer architecture has revolutionized computer vision. Learn Vision Transformers (ViT), DINO, SAM (Segment Anything), and multimodal models that combine vision and language (CLIP, LLaVA). Understand how these models achieve state-of-the-art performance and when to use them vs. traditional CNNs.

Vision Transformers (ViT) — adapting attention for images
DINO and DINOv2 — self-supervised visual representation learning
CLIP — connecting images and text for zero-shot recognition
Segment Anything Model (SAM) — universal image segmentation
Multimodal models — BLIP, LLaVA, GPT-4V for vision+language
Efficient architectures — MobileNet, EfficientNet for edge deployment
Papers Meta AI Research Reference Papers with Code: Computer Vision Free Models Hugging Face Vision Models
Transformer Attention Mechanism Computer Vision Multimodal AI Transfer Learning Zero-Shot & Few-Shot Learning Embedding
4

Generative Vision Models

3–4 weeks

Generative models are transforming computer vision — from image generation and editing to video synthesis and 3D reconstruction. Understand Diffusion Models (Stable Diffusion, DALL·E), GANs (StyleGAN), and how these models enable image inpainting, super-resolution, style transfer, and text-to-image generation.

Diffusion Models — forward process, reverse process, DDPM, score matching
Stable Diffusion architecture — U-Net, CLIP text encoder, VAE
ControlNet and guided generation — controlling image outputs
GANs — generator/discriminator, training dynamics, StyleGAN
Image-to-image translation — pix2pix, CycleGAN
Video generation and understanding — temporal models and architectures
3D vision — NeRFs, Gaussian Splatting, depth estimation
Free Course Hugging Face Diffusion Models Course Research Stability AI Research Free Tutorial The Annotated Diffusion Model
Diffusion Model Generative Adversarial Network (GAN) Generative AI Latent Space Deep Learning (DL)
5

Deployment & Real-Time Systems

3–4 weeks

Production CV systems require real-time performance, edge deployment, and robust handling of real-world conditions. Learn model optimization (quantization, pruning, ONNX), deployment on edge devices (NVIDIA Jetson, mobile), and building end-to-end CV pipelines with video streaming, tracking, and multi-camera systems.

Model optimization — quantization, pruning, knowledge distillation, TensorRT
ONNX Runtime — cross-platform model deployment
Edge deployment — NVIDIA Jetson, mobile (CoreML, TFLite), browser (TensorFlow.js)
Real-time video processing — multi-threading, GPU acceleration, streaming pipelines
Object tracking — SORT, DeepSORT, ByteTrack for video applications
MLOps for CV — data labeling pipelines, model versioning, A/B testing in production
Hardware/Docs NVIDIA Jetson Developer Kit Reference ONNX Runtime Documentation Tool Roboflow: End-to-End CV Platform
Edge AI Model Compression GPU & TPU Inference Computer Vision Benchmark API

Tools & Technologies

PyTorch / torchvision
OpenCV
Hugging Face
Ultralytics YOLO
NVIDIA TensorRT
Label Studio / CVAT

Career Outcomes

Computer Vision Engineer ($140K–$220K+)
Perception Engineer (autonomous vehicles)
Medical Imaging AI Specialist
AR/VR Computer Vision Developer

Frequently Asked Questions

Is computer vision still relevant with multimodal LLMs?

Absolutely. Multimodal LLMs (GPT-4V, Claude Vision) can describe images but can't run in real-time, process video streams, or deploy on edge devices with strict latency requirements. Autonomous vehicles, manufacturing inspection, medical imaging, and security systems all require specialized CV models. LLMs complement rather than replace traditional computer vision.

What hardware do I need for computer vision?

For learning: a modern laptop with a GPU (NVIDIA GTX 1060+ or RTX series) or free cloud GPUs (Google Colab, Kaggle). For serious development: NVIDIA RTX 3080/4080+ or cloud instances with A100/H100 GPUs. For edge deployment practice: NVIDIA Jetson Nano (~$150) or a smartphone.

What industries hire computer vision engineers?

Autonomous vehicles (Tesla, Waymo, Cruise), healthcare/medical imaging, manufacturing and quality control, retail (visual search, cashierless stores), security and surveillance, agriculture (crop monitoring), augmented reality (Apple, Meta), robotics, and satellite/geospatial intelligence.

Should I learn CNNs or Vision Transformers first?

Start with CNNs. They're conceptually simpler, widely used in production, and understanding convolutions is essential background for all of computer vision. Once comfortable with CNNs and transfer learning, move to Vision Transformers — they're increasingly dominant in research and state-of-the-art results but build on concepts you'll learn with CNNs.